1. Making a Basic Histogram

library(ggplot2)
library(gcookbook) # For the data set 

library(plyr) ##

ggplot( faithful, aes( x = waiting)) + geom_histogram()
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.

# If you just want to get a quick look at some data that isn’t in a data frame, you can get the same result by passing in NULL
w <- faithful$waiting 
ggplot( NULL, aes( x = w)) + geom_histogram()
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.

# You can change the size of the bins by using binwidth,
# Set the width of each bin to 5 
ggplot( faithful, aes( x = waiting)) + 
  geom_histogram( binwidth = 5, fill ="white", colour ="black")

# Divide the x range into 15 bins 
binsize <- diff( range( faithful$waiting))/ 15 
ggplot( faithful, aes( x = waiting)) + geom_histogram( binwidth = binsize, fill ="white", colour ="black")

h <- ggplot( faithful, aes( x = waiting)) 
# Save the base object for reuse 
h + geom_histogram( binwidth = 8, fill ="white", colour ="black", origin = 31) 

h + geom_histogram( binwidth = 8, fill ="white", colour ="black", origin = 35)

2. Making Multiple Histograms from Grouped Data

You want to make histograms of multiple groups of data.

library( MASS) # For the data set # Use smoke as the faceting variable 
ggplot( birthwt, aes( x = bwt)) + 
  geom_histogram( fill ="white", colour ="black") + facet_grid( smoke ~ .)
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.

## One problem with the faceted graph is that the facet labels are just 0 and 1, and there’s no label indicating that those values are for smoke.
birthwt1 <- birthwt # Make a copy of the data # Convert smoke to a factor 
birthwt1$smoke <- factor( birthwt1$smoke) 
levels( birthwt1$smoke)
## [1] "0" "1"
birthwt1$smoke <- revalue( birthwt1$smoke, c("0" =" No Smoke", "1" =" Smoke"))

ggplot( birthwt1, aes( x = bwt)) + 
  geom_histogram( fill ="white", colour ="black") + 
  facet_grid( smoke ~ .)
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.

ggplot( birthwt, aes( x = bwt)) + 
  geom_histogram( fill ="white", colour ="black") + 
  facet_grid( race ~ .)
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.

# To allow the y scales to be resized independently use scales =" free".
ggplot( birthwt, aes( x = bwt)) + 
  geom_histogram( fill ="white", colour ="black") + 
  facet_grid( race ~ ., scales ="free")
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.

# Convert smoke to a factor 
birthwt1$smoke <- factor( birthwt1$smoke)

# Map smoke to fill, make the bars NOT stacked, and make them semitransparent 
ggplot( birthwt1, aes( x = bwt, fill = smoke)) + 
  geom_histogram( position ="identity", alpha = 0.4)
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.

3. Making a Density Curve

# Use geom_density() and map a continuous variable to x
ggplot( faithful, aes( x = waiting)) + 
  geom_density()

# The expand_limits() increases the y range to include the value 0 
ggplot( faithful, aes( x = waiting)) + 
  geom_line( stat ="density") + 
  expand_limits( y = 0)

# Store the values in a simple vector
w <- faithful$waiting 
ggplot( NULL, aes( x = w)) +
  geom_density()

# kernel density curve is an estimate of the population distribution, based on the sample data. The amount of smoothing depends on the kernel bandwidth: the larger the bandwidth, the more smoothing there is. The bandwidth can be set with the adjust parameter, which has a default value of 1.
ggplot( faithful, aes( x = waiting)) + 
  geom_line( stat ="density", adjust =.25, colour ="red") + 
  geom_line( stat ="density") + 
  geom_line( stat ="density", adjust = 2, colour ="blue")

# area 
ggplot( faithful, aes( x = waiting)) + 
  geom_density( fill ="blue", alpha =.2) + 
  xlim( 35, 105)

# This draws a blue polygon with geom_density(), then adds a line on top 
ggplot( faithful, aes( x = waiting)) + 
  geom_density( fill ="blue", colour = NA, alpha =.2) + 
  geom_line( stat ="density") + 
  xlim( 35, 105)

# To compare the theoretical and observed distributions, you can overlay the density curve with the histogram.
ggplot( faithful, aes( x = waiting, y = ..density.. )) + 
  geom_histogram( fill ="cornsilk", colour ="grey60", size =.2) + 
  geom_density() + 
  xlim( 35, 105)
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.

4. Making Multiple Density Curves from Grouped Data

You want to make density curves of multiple groups of data.

library( MASS) # For the data set 

# Make a copy of the data 
birthwt1 <- birthwt 

# Convert smoke to a factor 
birthwt1$smoke <- factor( birthwt1$smoke) 

# Map smoke to colour 
ggplot( birthwt1, aes( x = bwt, colour = smoke)) + 
  geom_density() 

# Map smoke to fill and make the fill semitransparent by setting alpha 
ggplot( birthwt1, aes( x = bwt, fill = smoke)) + geom_density( alpha =.3)

# Another method for visualizing the distributions is to use facets,
ggplot( birthwt1, aes( x = bwt)) + 
  geom_density() + 
  facet_grid( smoke ~ .)

# to rename 0/1 into Smoke/No Smoke
birthwt1$smoke <- revalue( birthwt1$smoke, c("0" ="No Smoke", "1" ="Smoke"))
ggplot( birthwt1, aes( x = bwt)) + 
  geom_density() + 
  facet_grid( smoke ~ .)

# If you want to see the histograms along with the density curves,
ggplot( birthwt1, aes( x = bwt, y =..density..)) + 
  geom_histogram( binwidth = 200, fill ="cornsilk", colour ="grey60", size =.2) + 
  geom_density() + 
  facet_grid( smoke ~ .)

5. Making a Frequency Polygon

ggplot( faithful, aes( x = waiting)) + 
  geom_freqpoly()
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.

# you can control the bin width for the frequency polygon
ggplot( faithful, aes( x = waiting)) + 
  geom_freqpoly( binwidth = 4)

# Or, instead of setting the width of each bin directly,
# Use 15 bins 
binsize <- diff( range( faithful$waiting))/ 15 
ggplot( faithful, aes( x = waiting)) + geom_freqpoly( binwidth = binsize)

6. Making a Basic Box Plot

library( MASS) # For the data set 
ggplot( birthwt, aes( x = factor( race), y = bwt)) + 
  geom_boxplot() 

# To change the width of the boxes, you can set width
ggplot( birthwt, aes( x = factor(race), y = bwt)) + 
  geom_boxplot( width =.5)

# If there are many outliers and there is overplotting, you can change the size and shape of the outlier points with outlier.size and outlier.shape.
ggplot( birthwt, aes( x = factor( race), y = bwt)) + 
  geom_boxplot( outlier.size = 1.5, outlier.shape = 21)

# To make a box plot of just a single group,
ggplot( birthwt, aes( x = 1, y = bwt)) + 
  geom_boxplot() + 
  scale_x_continuous( breaks = NULL) + 
  theme( axis.title.x = element_blank())

7. Adding Notches to a Box Plot

You want to add notches to a box plot to assess whether the medians are different.

library( MASS) # For the data set 

ggplot( birthwt, aes( x = factor( race), y = bwt)) + 
  geom_boxplot( notch = TRUE)
## notch went outside hinges. Try setting notch=FALSE.

8. Adding Means to a Box Plot

library( MASS) # For the data set 
ggplot( birthwt, aes( x = factor( race), y = bwt)) + 
  geom_boxplot() + 
  stat_summary( fun.y ="mean", geom ="point", shape = 23, size = 3, fill ="white")

9. Making a Violin Plot

# Base plot 
p <- ggplot( heightweight, aes( x = sex, y = heightIn)) 
p + geom_violin()

# Additionally, the box plot outliers are not displayed, which we do by setting outlier.colour = NA.
p + 
  geom_violin() + 
  geom_boxplot( width =.1, fill ="black", outlier.colour = NA) + 
  stat_summary( fun.y = median, geom ="point", fill ="white", shape = 21, size = 2.5)

# It’s possible to keep the tails, by setting trim = FALSE
p + 
  geom_violin( trim = FALSE)

# Instead of equal areas, you can use scale =" count" to scale the areas proportionally to the number of observations in each group
p + 
  geom_violin( scale ="count")

# More smoothing 
p + 
  geom_violin( adjust = 2) 

# Less smoothing 
p + 
  geom_violin( adjust =.5)

10. Making a Dot Plot

You want to make a Wilkinson dot plot, which shows each data point.

countries2009 <- subset( countries, Year == 2009 & healthexp > 2000) 
p <- ggplot( countries2009, aes( x = infmortality))
 
# default 
p + 
  geom_dotplot()
## stat_bindot: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.

# The y-axis labels can be removed by using scale_y_continuous(). We’ll also use geom_rug() to show exactly where each data point is
p + 
  geom_dotplot( binwidth =.25) + 
  geom_rug() + 
  scale_y_continuous( breaks = NULL) + # Remove tick markers 
  theme( axis.title.y = element_blank()) # Remove axis label

# To use bins that are arranged with a fixed, regular spacing, like a histogram, use method =" histodot".
p + 
  geom_dotplot( method ="histodot", binwidth =.25) + 
  geom_rug() + 
  scale_y_continuous( breaks = NULL) + 
  theme( axis.title.y = element_blank())

# The dots can also be stacked centered, or centered in such a way that stacks with even and odd quantities stay aligned. This can by done by setting stackdir ="center" or stackdir ="centerwhole"

p + geom_dotplot( binwidth =.25, stackdir ="center") +
  scale_y_continuous( breaks = NULL) + 
  theme( axis.title.y = element_blank()) 

p + geom_dotplot( binwidth =.25, stackdir ="centerwhole") +
  scale_y_continuous( breaks = NULL) + 
  theme( axis.title.y = element_blank())

11. Making Multiple Dot Plots for Grouped Data

You want to make multiple dot plots from grouped data.

ggplot( heightweight, aes( x = sex, y = heightIn)) + 
  geom_dotplot( binaxis ="y", binwidth =.5, stackdir ="center")

ggplot( heightweight, aes( x = sex, y = heightIn)) + 
  geom_boxplot( outlier.colour = NA, width =.4) + 
  geom_dotplot( binaxis ="y", binwidth =.5, stackdir ="center", fill = NA)

# It’s also possible to show the dot plots next to the box plots,
ggplot( heightweight, aes( x = sex, y = heightIn)) + 
  geom_boxplot( aes( x = as.numeric( sex) + .2, group = sex), width =.25) + 
  geom_dotplot( aes( x = as.numeric( sex) - .2, group = sex), binaxis ="y", binwidth =.5, stackdir ="center") +
  scale_x_continuous( breaks = 1: nlevels( heightweight$sex), labels = levels( heightweight$sex))

12. Making a Density Plot of Two-Dimensional Data

# The base plot 
p <- ggplot( faithful, aes( x = eruptions, y = waiting)) 
p + 
  geom_point() + 
  stat_density2d()

# Contour lines, with "height" mapped to color 
p + 
  stat_density2d( aes( colour =..level..))

# Map density estimate to fill color 
p + 
  stat_density2d( aes( fill =..density..), geom ="raster", contour = FALSE)

# With points, and map density estimate to alpha
p + 
  geom_point() + 
  stat_density2d( aes( alpha =..density..), geom ="tile", contour = FALSE)

# we’ll use a smaller bandwidth in the x and y directions, so that the density estimate is more closely fitted (perhaps overfitted) to the data
p + 
  stat_density2d( aes( fill =..density..), geom ="raster", contour = FALSE, h = c(.5,5))